VaderLogRest Algorithm: An Ensemble Learning Approach for Sentiment Analysis on Vaccination Tweets

Analyzing the emotions about the vaccines and vaccination will help to successfully carry forward the vaccination trials and government policies towards epidemic control. The tweets featured information on the most common immunizations has recently been available all around the world. The method of natural language processing is the successful tool to investigate the reactions of the people to various immunizations. This paper proposes a ensemble learning model making use of the VADER lexicon, logistic regression, and random forest algorithm for sentiment analysis to understand and interpret the people's sentiments through the tweets. We utilize a collection of tweets in April to May 2021 to extract inferences about public views on vaccinations as they become more widely available during the COVID-19 pandemic. The classification output of the VADER algorithm is used as one more feature that helps to achieve better accuracy using the random forest algorithm. One more feature is added with the available features using logistic regression. Hence, the classification outputs of VADER and logistic regression improve the classification accuracy to 88% for positive-negative outputs and 84% for positive, neutral, and negative outputs.